Abstract
Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student’s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the low-difficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Here, we do not consider these required courses which students must enrolled.
References
Kuh, G.D., Kinzie, J., Buckley, J.A., Bridges, B.K., Hayek, J.C.: Piecing Together the Student Success Puzzle: Research, Propositions, and Recommendations: ASHE Higher Education Report, vol. 116. Wiley, Hoboken (2011)
Zhang, Y., Dai, H., Yun, Y., Liu, S., Lan, A., Shang, X.: Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl. Based Syst. 205, 106290 (2020)
Al-Shehri, H., et al.: Student performance prediction using support vector machine and k-nearest neighbor. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4. IEEE (2017)
Ma, Y., Zong, J., Cui, C., Zhang, C., Yang, Q., Yin, Y.: Dual path convolutional neural network for student performance prediction. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds.) WISE 2020. LNCS, vol. 11881, pp. 133–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34223-4_9
Zhang, Y., Yun, Y., Dai, H., Cui, J., Shang, X.: Graphs regularized robust matrix factorization and its application on student grade prediction. Appl. Sci. 10(5), 1755 (2020)
Zhang, Y., An, R., Cui, J., Shang, X.: Undergraduate grade prediction in Chinese higher education using convolutional neural networks. In: 11th International Learning Analytics and Knowledge Conference, LAK 2021, pp. 462–468 (2021)
Khasahmadi, A.H., Hassani, K., Moradi, P., Lee, L., Morris, Q.: Memory-based graph networks (2020). arXiv preprint arXiv:2002.09518
Meng, D., Zhao, Q., Jiang, L.: A theoretical understanding of self-paced learning. Inf. Sci. 414, 319–328 (2017)
Kabakchieva, D.: Student performance prediction by using data mining classification algorithms. Int. J. Comput. Sci. Manag. Res. 1(4), 686–690 (2012)
Amra, I.A.A., Maghari, A.Y.: Students performance prediction using KNN and Naïve Bayesian. In: 2017 8th International Conference on Information Technology (ICIT), pp. 909–913. IEEE (2017)
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4800–4810 (2018)
Acknowledgement
This research is funded by the National Natural Science Foundation of China (Grants No. 61802313, U1811262, 61772426), the Fundamental Research Funds for Central Universities (Grant No. G2018KY0301) and the education and teaching reform research project of Northwestern Polytechnical University (Grant No. 2020JGY23).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yun, Y., Dai, H., Cao, R., Zhang, Y., Shang, X. (2021). Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-030-78270-2_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78269-6
Online ISBN: 978-3-030-78270-2
eBook Packages: Computer ScienceComputer Science (R0)